Performance Comparison of New Heuristic With Genetic Algorithm in Parallel Flow Line Set Up

A new heuristic has been developed to solve the problem in parallel flow line scheduling. It involves the minimization of the makespan by the optimal allocation of a finite number of jobs to finite number of lines in the first phase and the optimal sequencing of allocated jobs in each line in the second phase. Here new heuristic and genetic algorithm for analyzing the parallel flow line scheduling are discussed and executed on a set of randomly generated problems. The results obtained for the test problems suggest that the developed new heuristic can be used successfully to solve large scale parallel flow line scheduling problems

Broadcast scheduling with data bundles

Broadcast scheduling has been extensively studied in wireless environments, where a base station broadcasts data to multiple users. Due to the sole wireless channel's limited bandwidth, only a subset of the needs may be satisfiable, and so maximizing total (weighted) throughput is a popular objective. In many realistic applications, however, data are dependent or correlated in the sense that the joint utility of a set of items is not simply the sum of their individual utilities. On the one hand, substitute data may provide overlapping information, so one piece of data item may have lower value if a second data item has already been delivered; on the other hand, complementary data are more valuable than the sum of their parts, if, for example, one data item is only useful in the presence of a second data item. In this paper, we define a data bundle to be a set of data items with possibly nonadditive joint utility, and we study a resulting broadcast scheduling optimization problem whose objective is to maximize the utility provided by the data delivered

New Partitioning Techniques and Faster Algorithms for Approximate Interval Scheduling

Interval scheduling is a basic problem in the theory of algorithms and a classical task in combinatorial optimization. We develop a set of techniques for partitioning and grouping jobs based on their starting and ending times, that enable us to view an instance of interval scheduling on many jobs as a union of multiple interval scheduling instances, each containing only a few jobs. Instantiating these techniques in dynamic and local settings of computation leads to several new results. For $(1+\varepsilon)$-approximation of job scheduling of $n$ jobs on a single machine, we obtain a fully dynamic algorithm with $O(\frac{\log{n}}{\varepsilon})$ update and $O(\log{n})$ query worst-case time. Further, we design a local computation algorithm that uses only $O(\frac{\log{n}}{\varepsilon})$ queries. Our techniques are also applicable in a setting where jobs have rewards/weights. For this case we obtain a fully dynamic algorithm whose worst-case update and query time has only polynomial dependence on $1/\varepsilon$, which is an exponential improvement over the result of Henzinger et al. [SoCG, 2020]. We extend our approaches for unweighted interval scheduling on a single machine to the setting with $M$ machines, while achieving the same approximation factor and only $M$ times slower update time in the dynamic setting. In addition, we provide a general framework for reducing the task of interval scheduling on $M$ machines to that of interval scheduling on a single machine. In the unweighted case this approach incurs a multiplicative approximation factor $2 - 1/M$

More Reliable Protein NMR Peak Assignment via Improved 2-Interval Scheduling

Protein NMR peak assignment refers to the process of assigning a group of \u201cspin systems\u201d obtained experimentally to a protein sequence of amino acids. The automation of this process is still an unsolved and challenging problem in NMR protein structure determination. Recently, protein backbone NMR peak assignment has been formulated as an interval scheduling problem, where a protein sequence of amino acids is viewed as a discrete time interval (the amino acids on one-to-one correspond to the time units of ), each subset S of spin systems that are known to originate from consecutive amino acids of is viewed as a \u201cjob\u201d j S , the preference of assigning S to a subsequence P of consecutive amino acids on is viewed as the profit of executing job j S in the subinterval of corresponding to P, and the goal is to maximize the total profit of executing the jobs (on a single machine) during . The interval scheduling problem is Max SNP-hard in general. Typically the jobs that require one or two consecutive time units are the most difficult to assign/schedule. To solve these most difficult assignments, we present an efficient 7/13-approximation algorithm. Combining this algorithm with a greedy filtering strategy for handling long jobs (i.e. jobs that need more than two consecutive time units), we obtained a new efficient heuristic for protein NMR peak assignment. Our study using experimental data shows that the new heuristic produces the best peak assignment in most of the cases, compared with the NMR peak assignment algorithms in the literature. The 7/13-approximation algorithm is also the first approximation algorithm for a nontrivial case of the classical (weighted) interval scheduling problem that breaks the ratio 2 barrier

Profit Maximization with Customer Satisfaction Control for Electric Vehicle Charging in Smart Grids

As the market of electric vehicles is gaining popularity, large-scale commercialized or privately-operated charging stations are expected to play a key role as a technology enabler. In this dissertation, we study the problem of charging electric vehicles at stations with limited charging machines and power resources. Our electric vehicle charging station is composed of a central controller, multiple charging machines, and a plurality of parking lots. Each parking lot has a plug connectable to an arbitrary charging machine through a switching bar system. The switching bar system allows the station owner to serve a larger number of customers at the same time by enabling dynamic connections, where the number of charging machines could be much less than the number of plugs. The central controller collects all the information provided by the customers in advance or on the fly and decides when to activate or de-activate a machine-to-plug connection, how fast the vehicles should be charged, and how much energy should be delivered to each vehicle. The purpose of this study is to develop a novel profit maximization framework for charging station operation in both offline and online charging scenarios, under certain customer satisfaction constraints. The main goal is to maximize the profit obtained by the station owner and provide a satisfactory charging service to the customers. The framework includes not only the vehicle scheduling and charging power control, but also the managing of user satisfaction factors, which are defined as the percentages of finished charging targets. The profit maximization problem is proved to be NP-complete in both scenarios, for which two-stage charging strategies are proposed to obtain efficient suboptimal solutions. Competitive analysis is also provided to analyze the performance of the proposed online two-stage charging algorithm against the offline counterpart under non-congested and congested charging scenarios. Finally, the simulation results show that the proposed two-stage charging strategies have remarkable performance gains compared to the exhaustive search and other conventional charging strategies with respect to not only the unified profit, but also other practical interests, such as the computational time, the user satisfaction factor, the percentage of electric vehicles serviced, the power consumption, the competitive ratio, and the load factor